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# Research Engineer - Pretraining Data

- **Company:** Prime Inc.
- **Location:** San Francisco, CA, United States (Remote available)
- **Salary:** $150,000.0 - $350,000.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Big Data, Computer Programming, Data Deduplication, Distributed Computing Environment, Distributed Systems, Information Extraction, Python (Programming Language), Raw Data, Management of Software Versions, Web Crawlers, Data Processing, Apache Spark, Document Classification
- **Published:** October 15, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=0705c8c739e6c19e

## About the Role

* Strong programming skills in Python and a record of building reliable data-processing or distributed systems.
 * Experience working with large datasets and managing the practical tradeoffs of storage, compute, throughput, and reproducibility.
 * Ability to diagnose data-quality issues and translate research goals into measurable checks and experiments.
 * Comfort owning an evolving technical problem end to end and working closely with researchers to test assumptions and improve the system.

Relevant experience may include:

 * Web crawling, document extraction, code-data collection, or large-scale corpus curation.
 * Deduplication, text classification, quality scoring, synthetic data, or training-data contamination analysis.
 * Data-mixture experiments, curriculum learning, or measuring how dataset changes affect model performance.

Nice to have

 * Experience preparing datasets for language-model pretraining, continued pretraining, or midtraining.
 * Work with distributed processing frameworks such as Spark, Ray, or comparable systems.
 * Experience with multilingual, mathematical, scientific, or repository-level code data.
 * Familiarity with tokenization, training data loaders, and the needs of large model-training runs.

Depth in data acquisition, scalable processing, or data research can be a strong starting point. We don't expect every candidate to have worked across all of them.

## Description

* Find, evaluate, and collect valuable training data from web, code, and technical sources.
 * Build scalable pipelines that turn raw data into training-ready corpora through extraction, cleaning, deduplication, and filtering.
 * Develop checks for data quality, coverage, and contamination, and use them to improve collection and processing.
 * Work with researchers on data mixtures and sampling strategies for pretraining and midtraining.
 * Prepare and evaluate data for long-context learning, reasoning, and coding, testing synthetic data where it proves useful.
 * Run controlled data experiments and use model evaluations to decide which sources, mixtures, and processing choices are worth scaling.
 * Make large-scale data processing efficient, reproducible, and recoverable, with clear source provenance and dataset versioning.